






from scipy.stats import pearsonr

import datasets


SCREAMING_SNAKE_CASE__   :      List[str]				=		'''
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.
The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.
'''


SCREAMING_SNAKE_CASE__   :      int				=		'''
Args:
    predictions (`list` of `int`): Predicted class labels, as returned by a model.
    references (`list` of `int`): Ground truth labels.
    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.

Returns:
    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.
    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.

Examples:

    Example 1-A simple example using only predictions and references.
        >>> pearsonr_metric = datasets.load_metric("pearsonr")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])
        >>> print(round(results[\'pearsonr\'], 2))
        -0.74

    Example 2-The same as Example 1, but that also returns the `p-value`.
        >>> pearsonr_metric = datasets.load_metric("pearsonr")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)
        >>> print(sorted(list(results.keys())))
        [\'p-value\', \'pearsonr\']
        >>> print(round(results[\'pearsonr\'], 2))
        -0.74
        >>> print(round(results[\'p-value\'], 2))
        0.15
'''


SCREAMING_SNAKE_CASE__   :      Dict				=		'''
@article{2020SciPy-NMeth,
author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
      Haberland, Matt and Reddy, Tyler and Cournapeau, David and
      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
      Kern, Robert and Larson, Eric and Carey, C J and
      Polat, Ilhan and Feng, Yu and Moore, Eric W. and
      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
      Harris, Charles R. and Archibald, Anne M. and
      Ribeiro, Antonio H. and Pedregosa, Fabian and
      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
      Computing in Python}},
journal = {Nature Methods},
year    = {2020},
volume  = {17},
pages   = {261--272},
adsurl  = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,					_KWARGS_DESCRIPTION			)
class     lowerCamelCase_					(   datasets.Metric			):







				def       A						(    self  ):
								"""simple docstring"""
								return datasets.MetricInfo(
								    description=_DESCRIPTION							,       citation=_CITATION							,       inputs_description=_KWARGS_DESCRIPTION							,       features=datasets.Features(
								        {
								            '''predictions''': datasets.Value('''float'''  ),
								            '''references''': datasets.Value('''float'''  ),
								        }  )							,       reference_urls=['''https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html''']							,       )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase=False  ):
								"""simple docstring"""
								if return_pvalue:
												__magic_name__		:Optional[Any]   =   pearsonr(__SCREAMING_SNAKE_CASE							,       __SCREAMING_SNAKE_CASE  )
												return {"pearsonr": results[0], "p-value": results[1]}
								else:
												return {"pearsonr": float(pearsonr(__SCREAMING_SNAKE_CASE							,       __SCREAMING_SNAKE_CASE  )[0]  )}














"""simple docstring"""





import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


_SCREAMING_SNAKE_CASE   :							str          =		logging.getLogger(__name__)






class 	a		(   __snake_case						):
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					"""summarization"""
    SCREAMING_SNAKE_CASE		:	Any		=					["""loss"""]
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					ROUGE_KEYS
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					"""rouge2"""






    def __init__(			self	:				Optional[int]			,		__SCREAMING_SNAKE_CASE	:				List[str]			,		**__SCREAMING_SNAKE_CASE	:				List[str]   )	->						Union[str, Any]:
      if hparams.sortish_sampler and hparams.gpus > 1:
        lowerCamelCase_       	=		False
      elif hparams.max_tokens_per_batch is not None:
        if hparams.gpus > 1:
          raise NotImplementedError('Dynamic Batch size does not work for multi-gpu training'   )
        if hparams.sortish_sampler:
          raise ValueError('--sortish_sampler and --max_tokens_per_batch may not be used simultaneously'   )

      super().__init__(__SCREAMING_SNAKE_CASE			,		num_labels=__SCREAMING_SNAKE_CASE			,		mode=self.mode			,		**__SCREAMING_SNAKE_CASE   )
      use_task_specific_params(self.model			,		'summarization'   )
      save_git_info(self.hparams.output_dir   )
      lowerCamelCase_       	=		Path(self.output_dir   ) / 'metrics.json'
      lowerCamelCase_       	=		Path(self.output_dir   ) / 'hparams.pkl'
      pickle_save(self.hparams			,		self.hparams_save_path   )
      lowerCamelCase_       	=		0
      lowerCamelCase_       	=		defaultdict(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		self.config.model_type
      lowerCamelCase_       	=		self.config.tgt_vocab_size if self.model_type == 'fsmt' else self.config.vocab_size

      lowerCamelCase_       	=		{
          "data_dir": self.hparams.data_dir,
          "max_source_length": self.hparams.max_source_length,
          "prefix": self.model.config.prefix or "",
      }
      lowerCamelCase_       	=		{
          'train': self.hparams.n_train,
          'val': self.hparams.n_val,
          'test': self.hparams.n_test,
      }
      lowerCamelCase_       	=		{k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

      lowerCamelCase_       	=		{
          'train': self.hparams.max_target_length,
          'val': self.hparams.val_max_target_length,
          'test': self.hparams.test_max_target_length,
      }
      assert self.target_lens["train"] <= self.target_lens["val"], F'''target_lens: {self.target_lens}'''
      assert self.target_lens["train"] <= self.target_lens["test"], F'''target_lens: {self.target_lens}'''
      if self.hparams.freeze_embeds:
        freeze_embeds(self.model   )
      if self.hparams.freeze_encoder:
        freeze_params(self.model.get_encoder()   )
        assert_all_frozen(self.model.get_encoder()   )

      lowerCamelCase_       	=		get_git_info()['repo_sha']
      lowerCamelCase_       	=		hparams.num_workers
      lowerCamelCase_       	=		None  # default to config
      if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer			,		__SCREAMING_SNAKE_CASE   ):
        lowerCamelCase_       	=		self.tokenizer.lang_code_to_id[hparams.tgt_lang]
        lowerCamelCase_       	=		self.decoder_start_token_id
      lowerCamelCase_       	=		(
          SeqaSeqDataset if hasattr(self.tokenizer			,		'prepare_seq2seq_batch'   ) else LegacySeqaSeqDataset
      )
      lowerCamelCase_       	=		False
      lowerCamelCase_       	=		self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
      if self.hparams.eval_max_gen_length is not None:
        lowerCamelCase_       	=		self.hparams.eval_max_gen_length
      else:
        lowerCamelCase_       	=		self.model.config.max_length
      lowerCamelCase_       	=		self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric






    def   UpperCamelCase			(			self	:				Dict			,		__SCREAMING_SNAKE_CASE	:				Dict[str, torch.Tensor]   )	->						Dict[str, List[str]]:
      lowerCamelCase_       	=		{
          k: self.tokenizer.batch_decode(v.tolist()   ) if 'mask' not in k else v.shape for k, v in batch.items()
      }
      save_json(__SCREAMING_SNAKE_CASE			,		Path(self.output_dir   ) / 'text_batch.json'   )
      save_json({k: v.tolist() for k, v in batch.items()}			,		Path(self.output_dir   ) / 'tok_batch.json'   )

      lowerCamelCase_       	=		True
      return readable_batch






    def   UpperCamelCase			(			self	:				Dict			,		__SCREAMING_SNAKE_CASE	:				int			,		**__SCREAMING_SNAKE_CASE	:				List[Any]   )	->						Any:
      return self.model(__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Optional[int]			,		__SCREAMING_SNAKE_CASE	:				List[int]   )	->						int:
      lowerCamelCase_       	=		self.tokenizer.batch_decode(
          __SCREAMING_SNAKE_CASE			,		skip_special_tokens=__SCREAMING_SNAKE_CASE			,		clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE   )
      return lmap(str.strip			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Any			,		__SCREAMING_SNAKE_CASE	:				dict   )	->						Tuple:
      lowerCamelCase_       	=		self.tokenizer.pad_token_id
      lowerCamelCase_	,      lowerCamelCase_       	=		batch['input_ids'], batch['attention_mask']
      lowerCamelCase_       	=		batch['labels']
      if isinstance(self.model			,		__SCREAMING_SNAKE_CASE   ):
        lowerCamelCase_       	=		self.model._shift_right(__SCREAMING_SNAKE_CASE   )
      else:
        lowerCamelCase_       	=		shift_tokens_right(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )
      if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
        lowerCamelCase_       	=		decoder_input_ids
        self.save_readable_batch(__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		self(__SCREAMING_SNAKE_CASE			,		attention_mask=__SCREAMING_SNAKE_CASE			,		decoder_input_ids=__SCREAMING_SNAKE_CASE			,		use_cache=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		outputs['logits']
      if self.hparams.label_smoothing == 0:
        # Same behavior as modeling_bart.py, besides ignoring pad_token_id
        lowerCamelCase_       	=		nn.CrossEntropyLoss(ignore_index=__SCREAMING_SNAKE_CASE   )

        assert lm_logits.shape[-1] == self.vocab_size
        lowerCamelCase_       	=		ce_loss_fct(lm_logits.view(-1			,		lm_logits.shape[-1]   )			,		tgt_ids.view(-1   )   )
      else:
        lowerCamelCase_       	=		nn.functional.log_softmax(__SCREAMING_SNAKE_CASE			,		dim=-1   )
        lowerCamelCase_	,      lowerCamelCase_       	=		label_smoothed_nll_loss(
            __SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		self.hparams.label_smoothing			,		ignore_index=__SCREAMING_SNAKE_CASE   )
      return (loss,)






    @property
    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						int:
      return self.tokenizer.pad_token_id






    def   UpperCamelCase			(			self	:				Dict			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]			,		__SCREAMING_SNAKE_CASE	:				Any   )	->						Dict:
      lowerCamelCase_       	=		self._step(__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		dict(zip(self.loss_names			,		__SCREAMING_SNAKE_CASE   )   )
      # tokens per batch
      lowerCamelCase_       	=		batch['input_ids'].ne(self.pad   ).sum() + batch['labels'].ne(self.pad   ).sum()
      lowerCamelCase_       	=		batch['input_ids'].shape[0]
      lowerCamelCase_       	=		batch['input_ids'].eq(self.pad   ).sum()
      lowerCamelCase_       	=		batch['input_ids'].eq(self.pad   ).float().mean()
      # TODO(SS): make a wandb summary metric for this
      return {"loss": loss_tensors[0], "log": logs}






    def   UpperCamelCase			(			self	:				Optional[Any]			,		__SCREAMING_SNAKE_CASE	:				Any			,		__SCREAMING_SNAKE_CASE	:				int   )	->						Dict:
      return self._generative_step(__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				str			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]="val"   )	->						Dict:
      self.step_count += 1
      lowerCamelCase_       	=		{k: torch.stack([x[k] for x in outputs]   ).mean() for k in self.loss_names}
      lowerCamelCase_       	=		losses['loss']
      lowerCamelCase_       	=		{
          k: np.array([x[k] for x in outputs]   ).mean() for k in self.metric_names + ['gen_time', 'gen_len']
      }
      lowerCamelCase_       	=		(
          generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
      )
      lowerCamelCase_       	=		torch.tensor(__SCREAMING_SNAKE_CASE   ).type_as(__SCREAMING_SNAKE_CASE   )
      generative_metrics.update({k: v.item() for k, v in losses.items()}   )
      losses.update(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		{F'''{prefix}_avg_{k}''': x for k, x in losses.items()}
      lowerCamelCase_       	=		self.step_count
      self.metrics[prefix].append(__SCREAMING_SNAKE_CASE   )  # callback writes this to self.metrics_save_path
      lowerCamelCase_       	=		flatten_list([x['preds'] for x in outputs]   )
      return {
          "log": all_metrics,
          "preds": preds,
          F'''{prefix}_loss''': loss,
          F'''{prefix}_{self.val_metric}''': metric_tensor,
      }






    def   UpperCamelCase			(			self	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]			,		__SCREAMING_SNAKE_CASE	:				List[str]   )	->						Dict:
      return calculate_rouge(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				List[str]			,		__SCREAMING_SNAKE_CASE	:				dict   )	->						dict:
      lowerCamelCase_       	=		time.time()

      # parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
      lowerCamelCase_       	=		self.model.generate(
          batch['input_ids']			,		attention_mask=batch['attention_mask']			,		use_cache=__SCREAMING_SNAKE_CASE			,		decoder_start_token_id=self.decoder_start_token_id			,		num_beams=self.eval_beams			,		max_length=self.eval_max_length			,		)
      lowerCamelCase_       	=		(time.time() - ta) / batch['input_ids'].shape[0]
      lowerCamelCase_       	=		self.ids_to_clean_text(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		self.ids_to_clean_text(batch['labels']   )
      lowerCamelCase_       	=		self._step(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		dict(zip(self.loss_names			,		__SCREAMING_SNAKE_CASE   )   )
      lowerCamelCase_       	=		self.calc_generative_metrics(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		np.mean(lmap(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )   )
      base_metrics.update(gen_time=__SCREAMING_SNAKE_CASE			,		gen_len=__SCREAMING_SNAKE_CASE			,		preds=__SCREAMING_SNAKE_CASE			,		target=__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE   )
      return base_metrics






    def   UpperCamelCase			(			self	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				Tuple			,		__SCREAMING_SNAKE_CASE	:				int   )	->						Any:
      return self._generative_step(__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				Optional[int]   )	->						Tuple:
      return self.validation_epoch_end(__SCREAMING_SNAKE_CASE			,		prefix='test'   )






    def   UpperCamelCase			(			self	:				Any			,		__SCREAMING_SNAKE_CASE	:				Optional[int]   )	->						SeqaSeqDataset:
      lowerCamelCase_       	=		self.n_obs[type_path]
      lowerCamelCase_       	=		self.target_lens[type_path]
      lowerCamelCase_       	=		self.dataset_class(
          self.tokenizer			,		type_path=__SCREAMING_SNAKE_CASE			,		n_obs=__SCREAMING_SNAKE_CASE			,		max_target_length=__SCREAMING_SNAKE_CASE			,		**self.dataset_kwargs			,		)
      return dataset






    def   UpperCamelCase			(			self	:				Tuple			,		__SCREAMING_SNAKE_CASE	:				str			,		__SCREAMING_SNAKE_CASE	:				int			,		__SCREAMING_SNAKE_CASE	:				bool = False   )	->						DataLoader:
      lowerCamelCase_       	=		self.get_dataset(__SCREAMING_SNAKE_CASE   )

      if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
        lowerCamelCase_       	=		dataset.make_sortish_sampler(__SCREAMING_SNAKE_CASE			,		distributed=self.hparams.gpus > 1   )
        return DataLoader(
            __SCREAMING_SNAKE_CASE			,		batch_size=__SCREAMING_SNAKE_CASE			,		collate_fn=dataset.collate_fn			,		shuffle=__SCREAMING_SNAKE_CASE			,		num_workers=self.num_workers			,		sampler=__SCREAMING_SNAKE_CASE			,		)

      elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
        lowerCamelCase_       	=		dataset.make_dynamic_sampler(
            self.hparams.max_tokens_per_batch			,		distributed=self.hparams.gpus > 1   )
        return DataLoader(
            __SCREAMING_SNAKE_CASE			,		batch_sampler=__SCREAMING_SNAKE_CASE			,		collate_fn=dataset.collate_fn			,		num_workers=self.num_workers			,		)
      else:
        return DataLoader(
            __SCREAMING_SNAKE_CASE			,		batch_size=__SCREAMING_SNAKE_CASE			,		collate_fn=dataset.collate_fn			,		shuffle=__SCREAMING_SNAKE_CASE			,		num_workers=self.num_workers			,		sampler=__SCREAMING_SNAKE_CASE			,		)






    def   UpperCamelCase			(			self	:				Dict   )	->						DataLoader:
      lowerCamelCase_       	=		self.get_dataloader('train'			,		batch_size=self.hparams.train_batch_size			,		shuffle=__SCREAMING_SNAKE_CASE   )
      return dataloader






    def   UpperCamelCase			(			self	:				int   )	->						DataLoader:
      return self.get_dataloader('val'			,		batch_size=self.hparams.eval_batch_size   )






    def   UpperCamelCase			(			self	:				int   )	->						DataLoader:
      return self.get_dataloader('test'			,		batch_size=self.hparams.eval_batch_size   )






    @staticmethod
    def   UpperCamelCase			(			__SCREAMING_SNAKE_CASE	:				List[Any]			,		__SCREAMING_SNAKE_CASE	:				Optional[int]   )	->						Dict:
      BaseTransformer.add_model_specific_args(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )
      add_generic_args(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )
      parser.add_argument(
          '--max_source_length'			,		default=1024			,		type=__SCREAMING_SNAKE_CASE			,		help=(
              'The maximum total input sequence length after tokenization. Sequences longer '
              'than this will be truncated, sequences shorter will be padded.'
          )			,		)
      parser.add_argument(
          '--max_target_length'			,		default=56			,		type=__SCREAMING_SNAKE_CASE			,		help=(
              'The maximum total input sequence length after tokenization. Sequences longer '
              'than this will be truncated, sequences shorter will be padded.'
          )			,		)
      parser.add_argument(
          '--val_max_target_length'			,		default=142			,		type=__SCREAMING_SNAKE_CASE			,		help=(
              'The maximum total input sequence length after tokenization. Sequences longer '
              'than this will be truncated, sequences shorter will be padded.'
          )			,		)
      parser.add_argument(
          '--test_max_target_length'			,		default=142			,		type=__SCREAMING_SNAKE_CASE			,		help=(
              'The maximum total input sequence length after tokenization. Sequences longer '
              'than this will be truncated, sequences shorter will be padded.'
          )			,		)
      parser.add_argument('--freeze_encoder'			,		action='store_true'   )
      parser.add_argument('--freeze_embeds'			,		action='store_true'   )
      parser.add_argument('--sortish_sampler'			,		action='store_true'			,		default=__SCREAMING_SNAKE_CASE   )
      parser.add_argument('--overwrite_output_dir'			,		action='store_true'			,		default=__SCREAMING_SNAKE_CASE   )
      parser.add_argument('--max_tokens_per_batch'			,		type=__SCREAMING_SNAKE_CASE			,		default=__SCREAMING_SNAKE_CASE   )
      parser.add_argument('--logger_name'			,		type=__SCREAMING_SNAKE_CASE			,		choices=['default', 'wandb', 'wandb_shared']			,		default='default'   )
      parser.add_argument('--n_train'			,		type=__SCREAMING_SNAKE_CASE			,		default=-1			,		required=__SCREAMING_SNAKE_CASE			,		help='# examples. -1 means use all.'   )
      parser.add_argument('--n_val'			,		type=__SCREAMING_SNAKE_CASE			,		default=500			,		required=__SCREAMING_SNAKE_CASE			,		help='# examples. -1 means use all.'   )
      parser.add_argument('--n_test'			,		type=__SCREAMING_SNAKE_CASE			,		default=-1			,		required=__SCREAMING_SNAKE_CASE			,		help='# examples. -1 means use all.'   )
      parser.add_argument(
          '--task'			,		type=__SCREAMING_SNAKE_CASE			,		default='summarization'			,		required=__SCREAMING_SNAKE_CASE			,		help='# examples. -1 means use all.'   )
      parser.add_argument('--label_smoothing'			,		type=__SCREAMING_SNAKE_CASE			,		default=0.0			,		required=__SCREAMING_SNAKE_CASE   )
      parser.add_argument('--src_lang'			,		type=__SCREAMING_SNAKE_CASE			,		default=''			,		required=__SCREAMING_SNAKE_CASE   )
      parser.add_argument('--tgt_lang'			,		type=__SCREAMING_SNAKE_CASE			,		default=''			,		required=__SCREAMING_SNAKE_CASE   )
      parser.add_argument('--eval_beams'			,		type=__SCREAMING_SNAKE_CASE			,		default=__SCREAMING_SNAKE_CASE			,		required=__SCREAMING_SNAKE_CASE   )
      parser.add_argument(
          '--val_metric'			,		type=__SCREAMING_SNAKE_CASE			,		default=__SCREAMING_SNAKE_CASE			,		required=__SCREAMING_SNAKE_CASE			,		choices=['bleu', 'rouge2', 'loss', None]   )
      parser.add_argument('--eval_max_gen_length'			,		type=__SCREAMING_SNAKE_CASE			,		default=__SCREAMING_SNAKE_CASE			,		help='never generate more than n tokens'   )
      parser.add_argument('--save_top_k'			,		type=__SCREAMING_SNAKE_CASE			,		default=1			,		required=__SCREAMING_SNAKE_CASE			,		help='How many checkpoints to save'   )
      parser.add_argument(
          '--early_stopping_patience'			,		type=__SCREAMING_SNAKE_CASE			,		default=-1			,		required=__SCREAMING_SNAKE_CASE			,		help=(
              '-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So'
              ' val_check_interval will effect it.'
          )			,		)
      return parser






class 	a		(   __snake_case						):
    SCREAMING_SNAKE_CASE		:	Union[str, Any]		=					"""translation"""
    SCREAMING_SNAKE_CASE		:	List[str]		=					["""loss"""]
    SCREAMING_SNAKE_CASE		:	str		=					["""bleu"""]
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					"""bleu"""






    def __init__(			self	:				str			,		__SCREAMING_SNAKE_CASE	:				Dict			,		**__SCREAMING_SNAKE_CASE	:				Tuple   )	->						Optional[Any]:
      super().__init__(__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		hparams.src_lang
      lowerCamelCase_       	=		hparams.tgt_lang






    def   UpperCamelCase			(			self	:				Optional[int]			,		__SCREAMING_SNAKE_CASE	:				List[Any]			,		__SCREAMING_SNAKE_CASE	:				List[str]   )	->						dict:
      return calculate_bleu(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )







def 	lowerCamelCase__			(				_lowerCamelCase			:						str						,							_lowerCamelCase			:						Tuple=None					) ->							SummarizationModule:
  Path(args.output_dir					).mkdir(exist_ok=_lowerCamelCase					)
  check_output_dir(_lowerCamelCase						,							expected_items=3					)

  if model is None:
    if "summarization" in args.task:
      lowerCamelCase_       	=		SummarizationModule(_lowerCamelCase					)
    else:
      lowerCamelCase_       	=		TranslationModule(_lowerCamelCase					)
  lowerCamelCase_       	=		Path(args.data_dir					).name
  if (
      args.logger_name == "default"
      or args.fast_dev_run
      or str(args.output_dir					).startswith('/tmp'					)
      or str(args.output_dir					).startswith('/var'					)
  ):
    lowerCamelCase_       	=		True  # don't pollute wandb logs unnecessarily
  elif args.logger_name == "wandb":
    from pytorch_lightning.loggers import WandbLogger

    lowerCamelCase_       	=		os.environ.get('WANDB_PROJECT'						,							_lowerCamelCase					)
    lowerCamelCase_       	=		WandbLogger(name=model.output_dir.name						,							project=_lowerCamelCase					)

  elif args.logger_name == "wandb_shared":
    from pytorch_lightning.loggers import WandbLogger

    lowerCamelCase_       	=		WandbLogger(name=model.output_dir.name						,							project=F'''hf_{dataset}'''					)

  if args.early_stopping_patience >= 0:
    lowerCamelCase_       	=		get_early_stopping_callback(model.val_metric						,							args.early_stopping_patience					)
  else:
    lowerCamelCase_       	=		False

  lowerCamelCase_       	=		args.val_metric == 'loss'
  lowerCamelCase_       	=		generic_train(
      _lowerCamelCase						,							_lowerCamelCase						,							logging_callback=SeqaSeqLoggingCallback()						,							checkpoint_callback=get_checkpoint_callback(
          args.output_dir						,							model.val_metric						,							args.save_top_k						,							_lowerCamelCase					)						,							early_stopping_callback=_lowerCamelCase						,							logger=_lowerCamelCase						,							)
  pickle_save(model.hparams						,							model.output_dir / 'hparams.pkl'					)
  if not args.do_predict:
    return model

  lowerCamelCase_       	=		''
  lowerCamelCase_       	=		sorted(glob.glob(os.path.join(args.output_dir						,							'*.ckpt'					)						,							recursive=_lowerCamelCase					)					)
  if checkpoints:
    lowerCamelCase_       	=		checkpoints[-1]
    lowerCamelCase_       	=		checkpoints[-1]
  trainer.logger.log_hyperparams(model.hparams					)

  # test() without a model tests using the best checkpoint automatically
  trainer.test()
  return model


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE   :							List[str]          =		argparse.ArgumentParser()
  _SCREAMING_SNAKE_CASE   :							Any          =		pl.Trainer.add_argparse_args(parser)
  _SCREAMING_SNAKE_CASE   :							Any          =		SummarizationModule.add_model_specific_args(parser, os.getcwd())

  _SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		parser.parse_args()

  main(args)




